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Forecasting Guide
Selecting the Approach
Combining Approaches
Judgmental Models
Delphi Method
Curve Fitting
Analogous Data
Time Series Models
Moving Average
Exponential Smoothing
Decomposition Models
Box-Jenkins Models
Leading Indicator
Input-Output Models
Envelope S-Curves

BASIC IDEA: One adaptation of the S-curve is known as the envelope S-curve, which takes into consideration successive generations of technologies that provide the same benefits. The term "envelope" refers to the curve that connects the tangents of the successive individual S-shaped curves.

PROCEDURE: Start by plotting the growth curves for successive generations of a technology. Try connecting the tangents of these curves to form an "envelope" and base the forecast on the extrapolation of the envelope curve.

EXAMPLE: In the graph below, the sales data for 4k and 16k microprocessors is shown. The dotted line represents the envelope for these two S-curves which can be used to forecast future generations of microprocessors.




• There are several other functional forms that can be used for curve fitting, including exponential, double logarithmic, etc. A difficult part of this application is finding the curve that will best fit the available data.

• Very subjective and there is the probability of large errors

• Major problems, including:

• What is appropriate shape of the curve

• Which stage is variable in at the present

• What are lengths of the curve(s)

• Theoretical or practical (including social, economic, etc.) limits to the curve


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